Multivariate analysis of geographically diverse rice germplasm for genetic improvement of yield, dormancy and shattering-related traits

Abstract

A diverse set of 107 rice genotypes was evaluated for yield, shattering and dormancy traits. Analysis of variance revealed sizable variation while skewness and kurtosis values indicated near-normal distribution for most of the traits, thus quantitative nature controlled by many genes. A highly significant deviation from a normal distribution for dormancy and shattering % indicated their qualitative nature of inheritance. Four promising genotypes ‘IRGC1723’ (early with 65 days to flowering), ‘IRGC 11108’ and ‘RNR 15459’ (high grain number – 358 and low average shattering – <5%), ‘RNR 11718’ (high single plant yield – 56.73 g, low average shattering – <5% and dormancy period – 21 days) are identified. A significant positive correlation between shattering and dormancy confirms inter-relationship among domestication-related characteristics. The principal component analysis revealed the contribution of four PCs to maximum variability and hierarchical clustering grouped the genotypes into 18 divergent clusters. Five cultivars (Karimnagar Samba, Sheetal, PR 121, Pranahitha and Jagitial Samba) with a combination of low shattering ability (3.35–5.7%) and considerable dormancy period (13–20 days) falling in the same cluster can be used as donors for the improvement of rice genotypes with low shattering ability and incorporating a considerable period of dormancy so as to avoid pre-harvest sprouting due to delayed harvesting. Further, they can be crossed with ‘Pratyumna’ having less than 1 week dormancy period, a genotype of the cluster XVII with which they have a maximum genetic divergence of 51.4 and may serve as parents in the development of mapping populations for the identification of QTLs/genes for shattering and dormancy traits.

Molecular and Morphological Characterization of Introgression Lines with Resistance to Bacterial Leaf Blight and Blast in Rice

“…Generally, highly significant negative correlation is observed between plant yield and plant height, which was also observed in the present study. Contrary to this, a significant positive correlation between yield and plant height was reported in other studies [ 50 , 51 , 52 , 53 ].…”

“…Principal component analysis (PCA), a multivariate technique, reduces data with a large number of correlated variables into a substantially smaller set of new variables through a linear combination of the variables that accounts for most of the variation present in the original variables. PCs explain the variability which could not be attributed to the other factors [ 50 ]. In the present investigation, the grouping of the ILs was mainly explained by the three major PCs, while a similar amount of cumulative variance with two to four major PCs was reported [ 50 , 54 ].…”

“…PCs explain the variability which could not be attributed to the other factors [ 50 ]. In the present investigation, the grouping of the ILs was mainly explained by the three major PCs, while a similar amount of cumulative variance with two to four major PCs was reported [ 50 , 54 ]. PCA biplots visualized the positive and negative associations among the component yield traits in the present study, suggesting the selection criteria for higher grain yield among the ILs.…”

Selection of Superior Genotypes of Lima Bean Landraces by Multivariate Approach

“…According to Deepika et al (2021), while UPGMA, a powerful tool, quantifies the genetic divergence in germplasm, principal component analysis (PCA) helps in the identification of a set of genotypes capturing maximum genetic diversity of the collection, and both are efficient in separating the accessions into genetically divergent clusters. Besides, PCA detected positive and significant phenotypic association between the variables: pod length, pod width and grain production.…”

Nutritional and Antinutritional Potentials of Sorghum: A Comparative Study among Different Sorghum Landraces of Tigray, Northern Ethiopia

“…On the other hand, the higher distance between the clusters tells that there is a wide variability between the landraces in the different clusters, which is important to conduct crossing to get better nutritional traits of interest. In other words, extremely divergent genotypes would yield a broad range of variability in the following generation [67]. Thus, desirable recombinants could be found by crossing parents selected from clusters IV and III and clusters IV and II.…”